Maria Knobelsdorf, University of Dortmund, Germany - Didaktik der ...

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groups of submissions that seem to have been set the same tasks. Because teacher peer support means that teaching material is shared, this does not necessarily mean that the similar papers have the same teachers or are even at the same school, although inevitably those who are in the same class probably were given the same assignment. For example, in one set of reports which had the same questions, several students compared Insertion sort with Selection sort, which will have a constant-factor difference. Many of the students correctly concluded that Insertion sort is twice as fast as Selection sort, but missed the opportunity to explore the possibility of a difference that was more than a constant factor. Some of the questions were presumably intended to stimulate discussion, but seemed to be treated by students as an exam, resulting in very short answers. For example, a series of questions about choices of programming languages in one group resulted in very brief answers of just a few words. Although the questions on programming languages may not have had the intended effect, the questions on usability for the same group resulted in generally good responses from the students. For this the rubric prompted the students to choose someone who isn’t a digital native (e.g. parent or grandparent) and observe them using a device for about an hour. One group that did particularly well had prompting questions that didn’t just mirror the criteria, but started off with a creative situation for the students to get them thinking beyond just checking off the criteria. For algorithms, the students were prompted to get the names of favourite songs from five friends, and then explain searching algorithms using that list. This provided a familiar context (searching for songs on an mp3 player), and a meaningful task (selecting a favourite song). It also ensured unique lists for each student, even though they were doing the same task. The usability section for this group began by asking students to describe the context the device is used in, and the role of the interface. Once again, this prompt doesn’t directly address the criteria, but it starts the students thinking of the big picture, and resulted in good quality answers. Now that the standard has been in use for a year, teachers will be able to share best practices based on how well students were able to respond to the various prompts. 5. CONCLUSION The analysis above has provided some specific ideas on how to teach and assess the three topics in the new standard. The following general observations are based on the analysis of student work. Teacher professional development and support is essential: Because the new standard was introduced very quickly, and because computer science is a new subject in New Zealand schools, teachers could not be expected to be well prepared to teach the new material, either in terms of subject knowledge or pedagogical knowledge. Unfortunately this was reflected in the student work, particularly in the guidance of topics they were given for their investigations (e.g. comparing two algorithms that didn’t have contrast- 18 ing performance, or investigating only small values of n). Teachers need to be guided on what the main messages are that students should take away from this experience, and be given recommendations of examples that illustrate these in a compelling way — in this case, contrasting algorithms, contrasting programming languages, and interfaces that are small enough to evaluate but have interesting usability issues. Students do better when they personalize their work: When students report on their own experiences, whether it is running an algorithm on their own computer or evaluating a device they have chosen, their work carries a lot more authenticity than abstract discussions of a concept. It also moves their level of understanding up Bloom’s taxonomy: descriptions of a concept often come across as a paraphrase that at best reflects remembering facts, while discussions based on a unique example reflect the student applying their knowledge. Assessment using reports: The AS91074 computer science standard is assessed by a student report submission. For most of the criteria this provides an opportunity for students to report on a rich personal experience, such as experiments with algorithms and evaluation of interface usability with real users. Through project work they get to experience the issues first-hand (such as a slow algorithm or a frustrated user), and get experience communicating in writing about computer science. However, some of the criteria (e.g. “describing... the function of a compiler”) invite responses that are just paraphrases of standard definitions, and would benefit from clarification or re-wording (e.g. “demonstrate the function of a compiler using an example”) to encourage students to explore the concept in a context rather than simply describe it. Using provided systems rather than having students build their own: The best student work was done when they assessed the performance of a provided implementation of an algorithm; students who implemented their own programs tended to be distracted by the implementation, were limited in the algorithms they could use, had potentially unreliable performance results, and were biased in their usability assessments. Because the standard is about algorithm performance and not programming, those who focussed on their own program were at risk of missing the big picture, or else might implement algorithms that didn’t show a good contrast. Likewise, students who discussed usability based on interfaces they had implemented couldn’t see their weaknesses. More advanced students might be expected to implement their own systems and evaluate them, but at the beginning high school level the messages about the key topics are likely to come across more clearly if students start with a working system. The introduction of computer science to NZ schools has addressed a need that was articulated by industry and teachers, and has happened remarkably quickly. The work done by students shows some pleasing levels of understanding of computer science for some; others have at least had experiences that indicate that they have had a taste of what kind of topics might come up in computer science courses. With very little lead-in time hundreds of students have studied
computer science, and with the lessons learned in the first year, the quantity and quality of students is only likely to increase in the future. 6. ACKNOWLEDGEMENTS We are grateful to Scott Telfer (NZQA) for assisting with access to key data. 7. REFERENCES [1] O. Astrachan. Bubble sort: An archaeological algorithmic analysis. ACM SIGCSE Bulletin, 35(1):1–5, 2003. [2] O. Astrachan, J. Cuny, C. Stephenson, and C. Wilson. The CS10K project: mobilizing the community to transform high school computing. Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, SIGCSE 2011, pages 85–86, 2011. [3] T. Bell, P. Andreae, and L. Lambert. Computer Science in New Zealand High Schools. In T. Clear and J. Hamer, editors, ACE ’10: Proceedings of the 12th conference on Australasian Computing Education, volume 32 of Australian Computer Science Communications, pages 15–22, Brisbane, Australia, Jan. 2010. Australian Computer Society, Inc. [4] T. Bell, P. Andreae, and A. Robins. Computer Science in NZ High Schools: The First Year of the New Standards. In L. A. S. King, D. R. Musicant, T. Camp, and P. Tymann, editors, Proceedings of the 43rd ACM technical symposium on Computer Science Education, Raleigh, NC, USA, pages 343–348, New York, 2012. ACM. [5] T. Brinda, H. Puhlmann, and C. Schulte. Bridging ICT and CS: Educational standards for computer science in lower secondary education. ACM SIGCSE Bulletin, 41(3):288–292, 2009. [6] A. Bruckman, M. Biggers, B. Ericson, T. McKlin, J. Dimond, B. DiSalvo, M. Hewner, L. Ni, and S. Yardi. “Georgia computes!”: Improving the Computing Education Pipeline. ACM SIGCSE Bulletin, 41(1):86–90, Mar. 2009. [7] T. Carrell, V. Gough-Jones, and K. Fahy. The future of Computer Science and Digital Technologies in New Zealand secondary schools: Issues of 21st teaching and learning, senior courses and suitable assessments. Technical report, 2008. [8] S. Furber, editor. Shut down or restart? The way forward for computing in UK schools. The Royal Society, London, 2012. [9] J. Gal-Ezer and C. Stephenson. The Current State of Computer Science in U.S. High Schools : A Report from Two National Surveys. Journal for Computing Teachers, Spring, 2009. [10] J. Goode and J. Margolis. Exploring Computer Science. ACM Transactions on Computing Education, 11(2):1–16, July 2011. [11] G. Grimsey and M. Phillipps. Evaluation of technology achievement standards for use in New Zealand secondary school computing education. Technical report, New Zealand Computer Society (NZCS), Wellington, 2008. [12] R. E. Pattis. Textbook errors in binary searching. ACM SIGCSE Bulletin, 20(1):190–194, Feb. 1988. 19 [13] A. Robins. Learning edge momentum: A new account of outcomes. Computer Science Education, 20:37 – 71, 2010.
Page 1 and 2: Fakultät für Mathematik, Informat
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attended by about a 33-40 % of all
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The assumption of sphericity was no
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[23] Huynh, H. and Feldt, L. S. 197
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Ways of Planning Lessons on the Top
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other topic. The reasons given vari
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Promoting Computational Thinking wi
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algorithm or a product is viewed as
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Preparing Teachers for Teaching Inf
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However, even when such learning th
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Grand challenges for the UK: Upskil
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first two questions. We maintain th
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(Some) Grand Challenges of Computer
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ecoming more student-centered. Inde
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[13] OECD. (2006). Are students rea
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in the world, for example, as descr
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The aims of the case study were to
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Figure 8: Acquisition of programmin
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The focus group girls were more con
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tool for teachers and students in t
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eledSQL - A New Web-Based Learning
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create and manage teacher accounts.
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ABSTRACT Bringing Contexts into the
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3.1.1 Greenfoot as a framework for
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Real-world context Views of Computi
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Real-world context GP Context Artif
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i. e. theories, models, frameworks,
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number P-156 in Lecture Notes in In
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1. Context-based education in compu
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Figure 1. Network traffic for authe
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A weak point of the Vigenère algor
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We have learnt that we can both loo
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Comparing CSTA K-12 Computer Scienc
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grade 4, 5, 6 or 8, depending on ch
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Table 1. Scoring Scale for the Impl
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the highest rate of implementation
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Information Theory on Czech Grammar
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of information in a message M, repr
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Data modeling and database systems
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the knowledge about ICT and CS (see
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The Mindstorm Effect: A Gender Anal
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Exploring the processing of formatt
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Teachersʼ Perceptions Of The Value
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Technocamps: Bringing Computer Scie
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Abenteuer Informatik - Hands-on exh
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Gaming and Mathematics: A Cross Cur
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Turi: Chatbot software for schools
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ABSTRACT Learning Fields in Vocatio
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ABSTRACT This study examines the po
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